The EpisTwin: A Knowledge Graph-Grounded Neuro-Symbolic Architecture for Personal AI

Authors:
  • GSGiovanni Servedio
    Giovanni Servedio
  • PAPotito Aghilar
    Potito Aghilar
  • AMAlessio Mattiace
    Alessio Mattiace
  • GCGianni Carmosino
    Gianni Carmosino
  • FMFrancesco Musicco
    Francesco Musicco
  • GCGabriele Conte
    Gabriele Conte
  • VAVito Walter Anelli
    Vito Walter Anelli
  • TNTommaso Di Noia
    Tommaso Di Noia
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Abstract
Personal Artificial Intelligence is currently hindered by the fragmentation of user data across isolated silos. While Retrieval-Augmented Generation offers a partial remedy, its reliance on unstructured vector similarity fails to capture the latent semantic topology and temporal dependencies essential for holistic sensemaking. We introduce EpisTwin, a neuro-symbolic framework that grounds generative reasoning in a verifiable, user-centric Personal Knowledge Graph. EpisTwin leverages Multimodal Language Models to lift heterogeneous, cross-application data into semantic triples. At inference, EpisTwin enables complex reasoning over the personal semantic graph via an agentic coordinator that combines Graph Retrieval-Augmented Generation with Online Deep Visual Refinement, dynamically re-grounding symbolic entities in their raw visual context. We also introduce PersonalQA-71-100, a synthetic benchmark designed to simulate a realistic user's digital footprint and evaluate EpisTwin performance. Our framework demonstrates robust results across a suite of state-of-the-art judge models, offering a promising direction for trustworthy Personal AI.
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